Sparseness and expansion in sensory representations.

By Baktash Babadi, Haim Sompolinsky. 2014

263 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Sensory Systems

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Citations in the corpus

NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks (2022, background): “Sparse connectivity: In contrast to DNNs’ dense and highly entangled connectivity that is prone to interference (French, 1999), the brain relies on sparse connectivity in which only few neurons respond to any given stimulus (Babadi & Sompolinsky, 2014).”
Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Structured connectivity may also be used to increase the signal-to-noise ratio (SNR) of the circuit, in order to separate activity patterns while constraining noise (Babadi and Sompolinsky, 2014).”
Recognition (2003, neutral): “Previous olfactory computation literature has explored robustness and associative learning (Babadi & Sompolinsky, 2014; Aso et al., 2014).”
Origins of Cell-Type-Specific Olfactory Processing in the Drosophila Mushroom Body Circuit. (2017, unknown): “This functional organization of the MB circuit is advantageous in that it represents odors distinctively in a high-dimensional coding space and thus can enhance odor discriminability (Babadi and Sompolinsky, 2014; Litwin-Kumar et al., 2017).”
Why neurons mix: high dimensionality for higher cognition. (2016, background): “in [35–39,49]), without compromising the ability to generalize [50,51].”
Cortical feedback decorrelates olfactory bulb output in awake mice (2015, background): “Within this framework, the piriform cortex may act as a pattern-recognition device (Babadi and Sompolinsky, 2014; Barak et al., 2013; Caron et al., 2013; Haberly, 2001; Haberly and Bower, 1989; Linster and Hasselmo, 2001) that compares incoming sensory inputs with representations of previously…”
Sequential and efficient neural-population coding of complex task information (2019, unknown): “Our points above about the theoretical utility of random sequential neural codes extends the literature on how neural circuits can exploit random designs to perform interesting computational functions, such as separable neural representations (Babadi and Sompolinsky, 2014; Fusi et al., 2016; Lindsay”
Optimal Architectures in a Solvable Model of Deep Networks (2016, methodology): “A similar model of clustered inputs fed into a single processing layer has been studied in [1] using a simpler, Hebbian projection weights.”
Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, unknown): “Finally, while the classical theory of granule cell function (Albus, 1971; Marr, 1969) focused on dimensionality expansion, a modern reanalysis of this theory indicates that maximal dimensionality expansion is not always preferable, as such expansion alone can also amplify input noise (Babadi and So”
Optimal degrees of synaptic connectivity (2017, unknown): “We considered the simple case of learning associations between outputs and random, uncorrelated input patterns with a fixed coding level, a standard benchmark for learning systems (Gardner, 1988; Barak et al., 2013; Babadi and Sompolinsky, 2014).”